
Journal Of Big Data, Год журнала: 2025, Номер 12(1)
Опубликована: Май 31, 2025
Язык: Английский
Journal Of Big Data, Год журнала: 2025, Номер 12(1)
Опубликована: Май 31, 2025
Язык: Английский
Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 159 - 180
Опубликована: Янв. 17, 2025
Time series forecasting is crucial for various real-world applications, such as energy consumption, traffic flow estimation, and financial market analysis. This chapter explores the application of deep learning models, specifically transformer-based models long-term time forecasting. Despite success transformers in sequence modeling, their permutation-invariant nature can lead to loss temporal information, posing challenges accurate Especially, embedding position-wise vector or time-stamp key long Another noted headache standard model squared computation complexity. studies development research field timer forecasting, challenging pain point, popular data sets, state-of-the-art benchmarks. The discussion covers implications, limitations, future directions, offering insights applying these advanced techniques problems.
Язык: Английский
Процитировано
0Advances in finance, accounting, and economics book series, Год журнала: 2025, Номер unknown, С. 461 - 488
Опубликована: Янв. 22, 2025
Recent studies underscore the growing application of machine learning (ML) in finance, as revealed through bibliometric analyses. Kureljusic and Karger (2024) reviewed AI-based forecasting financial accounting, identifying gaps proposing future research agendas. Similarly, Biju et al. (2023) explored taxonomy AI, deep learning, ML highlighting rise publications need for empirical on algorithmic technologies. Building this foundation, study presents a scientometric analysis data from 1996 to 2024. Using Scopus Web Science, we examine key themes, collaboration networks, influential contributors. Employing tools like Sentence-BERT, BerTopic, BERT, ChatGPT, PEGASUS, offer insights into how has reshaped forecasting. This provides basis research, guiding scholars practitioners towards impactful areas finance.
Язык: Английский
Процитировано
0Опубликована: Авг. 20, 2024
Machine learning (ML) has transformed the financial industry by enabling advanced applications such as credit scoring, fraud detection, and market forecasting. At core of this transformation is deep (DL), a subset ML that robust at processing analyzing complex large datasets. This paper provides concise overview key models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Deep Belief (DBNs), Transformers, Generative Adversarial (GANs), Reinforcement Learning (Deep RL). The study examines their processes, mathematical foundations, practical in finance. It also explores recent advances emerging trends alongside critical challenges data quality, model interpretability, computational complexity, offering insights into future research directions can guide development more explainable models.
Язык: Английский
Процитировано
3Annals of Operations Research, Год журнала: 2025, Номер unknown
Опубликована: Фев. 18, 2025
Язык: Английский
Процитировано
0Systems and Soft Computing, Год журнала: 2025, Номер unknown, С. 200209 - 200209
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
0Symmetry, Год журнала: 2025, Номер 17(3), С. 401 - 401
Опубликована: Март 7, 2025
Given the significant potential of large language models (LLMs) in sequence modeling, emerging studies have begun applying them to time-series forecasting. Despite notable progress, existing methods still face two critical challenges: (1) their reliance on amounts paired text data, limiting model applicability, and (2) a substantial modality gap between time series, leading insufficient alignment suboptimal performance. This paper introduces Hierarchical Text-Free Alignment (TS-HTFA) novel method that leverages hierarchical fully exploit representation capacity LLMs for analysis while eliminating dependence data. Specifically, data are replaced with adaptive virtual based QR decomposition word embeddings learnable prompts. Furthermore, comprehensive cross-modal is established at three levels: input, feature, output, contributing enhanced semantic symmetry modalities. Extensive experiments multiple benchmarks demonstrate TS-HTFA achieves state-of-the-art performance, significantly improving prediction accuracy generalization.
Язык: Английский
Процитировано
0Journal of Forecasting, Год журнала: 2025, Номер unknown
Опубликована: Март 16, 2025
ABSTRACT This study proposes a novel deep auto‐optimized architecture for stock price forecasting that integrates sectoral behavior with individual sentiment to improve predictive accuracy. Traditional prediction models often focus solely on behavior, overlooking the impact of broader trends. The proposed approach utilizes advanced learning models, including gated recurrent units (GRU), bidirectional GRU, long short‐term memory (LSTM), and LSTM, their hybrid ensembles. These are built using Keras functional API auto ML network search technology. current multimodal framework incorporates significantly improving performance metrics. research highlights critical role integrating in models.
Язык: Английский
Процитировано
0Information Sciences, Год журнала: 2025, Номер unknown, С. 122134 - 122134
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0Опубликована: Апрель 4, 2025
Язык: Английский
Процитировано
0Lecture notes in business information processing, Год журнала: 2025, Номер unknown, С. 3 - 17
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
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